• The Journal of Asian Finance, Economics and Business
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• 제8권1호
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• pp.479-486
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• 2021

This study aims to explore the relationships between strategic planning, strategic maneuverability, and firm performance in the current dynamic business environment. It employs a quantitative research method and reports on a survey, using a questionnaire, of service companies in Indonesia's oil and gas industry. Of the 337 companies selected by simple random sampling from a vendor database, responses were received from 70 companies. The analysis was performed using Partial Least Square Structural Equation Modeling and SmartPLS software. The analysis consisted of descriptive statistics, evaluation of the measurement model, evaluation of the structural model, and hypotheses testing. The results show that both strategic planning and strategic maneuverability have a positive relationship with firm performance. In addition, there is a positive relationship between strategic planning and firm performance through the mediating role of strategic maneuverability. The findings suggest that the organizational agility, organizational flexibility, and organizational responsiveness that constitute strategic maneuverability have a positive direct and indirect effect on firm performance, namely financial performance, customer performance, internal process performance, and learning and growth. This study contributes to the strategic management literature and the theory of maneuvers by providing empirical evidence on the relationship between strategic planning, strategic maneuverability, and firm performance.

• 드라이브 ㆍ 컨트롤
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• 제19권4호
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• pp.97-103
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• 2022

Low fertility rates and increased life expectancy further exacerbate the process of an aging society. This is also reflected in the gradual increase in the proportion of vulnerable groups in the social population. The demand for improved mobility among vulnerable groups such as the elderly or the disabled has greatly driven the growth of the electric-assisted mobility device market. However, such mobile devices generally require a certain operating capability, which limits the range of vulnerable groups who can use the device and increases the cost of learning. Therefore, autonomous driving technology needs to be introduced to make mobility easier for a wider range of vulnerable groups to meet their needs of work and leisure in different environments. This study uses mini PC Odyssey, Velodyne Lidar VLP-16, electronic device and Linux-based ROS program to realize the functions of working environment recognition, simultaneous localization, map generation and navigation of electric powered mobile devices for vulnerable groups. This autonomous driving mobility device is expected to be of great help to the vulnerable who lack the immediate response in dangerous situations.

• Smart Structures and Systems
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• 제29권1호
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Smart Structures and Systems
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• 제29권2호
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• pp.361-374
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• 2022

As civil infrastructure has continued to age worldwide, its structural integrity has been threatened owing to material deteriorations and continual loadings from the external environment. Structural Health Monitoring (SHM) has emerged as a cost-efficient method for ensuring structural safety and durability. As SHM research has gradually addressed an increasing number of structure-related problems, it has become difficult to understand the changing research topic trends. Although previous review papers have analyzed research trends on specific SHM topics, these studies have faced challenges in providing (1) consistent insights regarding macroscopic SHM research trends, (2) empirical evidence for research topic changes in overall SHM fields, and (3) methodological validations for the insights. To overcome these challenges, this study proposes a framework tailored to capturing the trends of research topics in SHM through a bibliometric and network analysis. The framework is applied to track SHM research topics over 15 years by identifying both quantitative and relational changes in the author keywords provided from representative SHM journals. The results of this study confirm that overall SHM research has become diversified and multi-disciplinary. Especially, the rapidly growing research topics are tightly related to applying machine learning and computer vision techniques to solve SHM-related issues. In addition, the research topic network indicates that damage detection and vibration control have been both steadily and actively studied in SHM research.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.149-156
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• 2023

Joint research on software, electronic engineering, computer engineering, and financial engineering and the use of ICT knowledge through network formation play an important role in strengthening science and technology-based innovation capabilities and facilitating the development and production process of products using new technologies. For the purpose of this study, I would like to strategically propose ICT specialized education in the 4th industrial revolution. To this end, the ICT specialization model, ICT specialization strategy analysis, and ICT specialization operation and effect were explored to establish ICT specialization strategies centered on software, electronic engineering, computer engineering, and financial engineering in the era of super-intelligence, hyper-connected, and hyper-convergence. Secondly, a roadmap for detailed promotion tasks related to efficient ICT characterization based on core strategies, detailed promotion tasks, and programs was proposed, focusing on talent related to ICT characterization. Thirdly, we would like to propose a reorganization of the academic structure and organization related to ICT characterization. Finally, we would like to propose the establishment of a future-oriented education system related to ICT specialization based on the advanced education and research environment.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.378-385
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• 2023

The importance of wearing respiratory protective equipment has been highlighted even more during the COVID-19 pandemic. Even if the suitability of respiratory protection has been confirmed through testing in a laboratory environment, there remains the potential for leakage points in the respirators due to improper application by the wearer, damage to the equipment, or sudden movements in real working conditions. In this paper, we propose a method to detect the occurrence of leak holes by measuring the pressure changes inside the mask according to the wearer's breathing activity by attaching an IoT sensor to a full-face respirator. We designed 9 experimental scenarios by adjusting the degree of leak holes of the respirator and the breathing cycle time, and acquired respiratory data for the wearer of the respirator accordingly. Additionally, we analyzed the respiratory data to identify the duration and pressure change range for each breath, utilizing this data to train a neural network model for detecting leak holes in the respirator. The experimental results applying the developed neural network model showed a sensitivity of 100%, specificity of 94.29%, and accuracy of 97.53%. We conclude that the effective detection of leak holes can be achieved by incorporating affordable, small-sized IoT sensors into respiratory protective equipment.

• 방송공학회논문지
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• 제27권6호
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• pp.923-935
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• 2022

본 논문에서는 스마트팜 시스템에서 재배 중인 식물 잎의 질병을 검출하고, 질병 유형을 분류하는 방법을 제안한다. 영상으로부터식물 잎의 컬러 정보와 질병 유형의 형태 정보를 다층 퍼셉트론(MLP) 모델을 이용하여 학습한다. 1단계에서는 입력된 영상의 컬러분포를 분석하여 질병 존재 여부를 판단한다. 1단계의 질병 존재 가능성이 높은 영상에 대하여 2단계에서는 Mean shift clustering을 이용하여 작은 영역으로 분할하고, 각 분할된 영역 단위로 컬러 정보를 추출하여 제안한 Color Network에 의하여 질병 여부를 판별한다. 컬러 분할된 영역이 Color Network에 의하여 질병으로 판별되면, 3단계에서는 그 영역의 형태 정보를 추출하여 제안한 Shape Network를 이용하여 질병의 유형을 분류한다. 사과나무 잎과 서양 양상추(Iceberg)에서 발생하는 두 가지 대분류 유형의 질병에 대하여, 제안한 기법은 작은 영역 단위로는 92.3%의 잎 질병 검출률을 보였으며, 보통 2개 이상의 질병 영역이 존재하는 한 장의 영상 단위로는 99.3% 이상의 검출률을 보였다. 본 논문에서 제안한 방법은 스마트팜 환경에서 잎 식물의 질병 여부를 조기에 발견할 수 있으며, 대상 식물에 따른 추가 학습 없이 다양한 식물과 질병 유형으로 확대 적용이 가능하다.

• 스마트미디어저널
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• 제12권10호
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• pp.93-101
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• 2023

최근 기온 상승, 가뭄, 홍수 등 기후변화가 세계적인 문제로 대두되고 있으며, 농업분야에서는 작물의 특성과 생산성에 많은 영향을 미칠 것으로 예측하고 있다. 천궁은 전통적으로 사용되는 한약재뿐만 아니라 건강기능식품, 천연물의약품, 생활소재 등 다양한 산업적 원료로 활용되고 있으나, 연작장해, 기후변화 등 위협 요인으로 인한 생산성이 감소되고 있다. 그러므로 본 논문에서는 기후변화에 취약한 대표 약용 작물인 천궁의 기후변화 시나리오에 따른 생리 활성 성분 지표를 예측할 수 있는 모델을 제안한다. 먼저 기상 정보와 생리 반응, 생리 활성 성분 정보의 수집 데이터 불균형 문제를 해결하기 위해 CTGAN 알고리즘을 이용하여 데이터를 증강하였다. 증강 데이터 품질 측정을 위해 Column Shape, Column Pair Trends를 이용하였으며 평균 88% Overall Quality를 달성하였다. 증강 데이터를 이용하여 지상부와 지하부로 나누어 페놀과 플라보노이드 함량을 예측하기 위해 5가지 모델 RF, SVR, XGBoost, AdaBoost, LightBGM을 이용하여 평가하였다. 모델 성능 평가 결과 XGBoost 모델이 천궁 생리 활성 성분 예측에 가장 우수한 성능을 보였으며, SVR 모델 대비 약 2배 정도의 향상된 정확도를 확인할 수 있었다.

• Smart Structures and Systems
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• 제29권1호
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• 인터넷정보학회논문지
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• 제25권2호
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• pp.49-55
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• 2024

본 논문은 물고기 양식 전문가의 먹이 배급을 모방하는 신경망 모델인 PredFeed Net을 제안한다. PredFeed Net은 기존의 먹이 배급 자동화 시스템과 달리, 전문가의 먹이 배급 패턴을 학습하는 방식으로 먹이 배급량을 예측한다. 이는 실제 수조에서 환경에 따른 먹이 배급 변수를 바꾸며 실험할 필요 없이, 기존의 환경 데이터와 먹이 배급 전문가의 먹이 배급 기록만으로 학습이 가능하다는 이점이 있다. 학습이 완료된 PredFeed Net은 현재 환경이나 어류의 상태를 통해 다음 먹이 배급량을 예측한다. 먹이 배급량 예측은 먹이 배급 자동화에 필요한 요소이며, 먹이 배급 자동화는 스마트 양식업이나 아쿠아포닉스 시스템 같은 최신 양식어업에 발전에 기여한다.